Features-based 2d-3d image registration

ABSTRACT

An image registration apparatus comprises: a features detector ( 34 ) configured to extract a two-dimensional set of features ( 36 ) from a two-dimensional image ( 30 ) and to extract a three-dimensional set of features ( 38 ) from a three-dimensional image ( 32 ); a projection processor ( 40 ) configured to project three-dimensional data into two-dimensional projection data; and a registration processor ( 46, 52 ) configured to (i) adjust parameters to register the two-dimensional set of features and the three-dimensional set of features projected by the projection processor using a projection geometry ( 42 ), and to (ii) use the adjusted parameters to register the two-dimensional image and the three-dimensional image projected by the projection processor using the projection geometry.

The following relates to the medical imaging arts. In some embodimentsit relates to registering two-dimensional (2D) x-ray fluoroscopy imageswith three-dimensional (3D) images acquired by computed tomography,magnetic resonance imaging, or another imaging modality. More generally,however, the following relates to registering two-dimensional imagesacquired by any medical imaging modality with three-dimensional imagesacquired by the same or a different medical imaging modality.

In medical imaging procedures, it is sometimes the case that relevantimaging data is acquired using both two-dimensional andthree-dimensional imaging. In some such cases, it is useful to generateand register a two-dimensional representation of the three-dimensionalimage with a corresponding two-dimensional image, so as to compare orcombine information provided by the two techniques.

One example sometimes arises in interventional cardiacelectrophysiology. During this procedure, x-ray fluoroscopy is sometimesused to visualize catheters or other interventional instruments.Advantageously, x-ray fluoroscopy images can be acquired using a “C-arm”type apparatus in which the x-ray tube and x-ray detector are mounted onopposite ends of the C-arm, and the patient is disposed in the gap. AC-arm type apparatus is relatively open, thus making the patient readilyaccessible to medical personnel. However, some soft tissue anatomy isnot effectively imaged by x-ray fluoroscopy. Further, fluoroscopicimages are typically acquired at a low x-ray dose, which can compromiseresolution.

Accordingly, it is known to acquire pre-operative images of the patientbefore undergoing the cardiac electrophysiology procedure, using athree-dimensional imaging technique such as multi-slice computedtomography (CT) or magnetic resonance imaging (MRI), either of whichprovide better soft tissue contrast than x-ray fluoroscopy. Thepre-operatively acquired CT or MRI images are then fused with the x-rayfluoroscopic images acquired during the cardiac electrophysiologyprocedure so that the CT or MRI images provide the missing soft tissuecontrast.

CT or MRI images are typically generated for a three-dimensional volume;whereas, the x-ray fluoroscopy images are two-dimensional. It is knownto mathematically project a three-dimensional image into atwo-dimensional image using ray-casting techniques. Applying ray castingto the CT or MRI image produces a two-dimensional image. However, themathematically projected CT or MRI image is generally not spatiallyregistered with the x-ray fluoroscopic image, because the projectiongeometry of the x-ray fluoroscope respective to the patient generallydiffers from the projection geometry used in the mathematical generationof the CT or MRI projection. In some cases, further error may result dueto distortions or other imperfections or artifacts in the x-rayfluoroscopic image and/or in the three-dimensional CT or MRI image.

The following provides improvements, which overcome the above-referencedproblems and others.

An image registration process is disclosed, comprising: extracting atwo-dimensional set of features from a two-dimensional image; extractinga three-dimensional set of features from a three-dimensional image;mathematically projecting the three-dimensional set of features into atwo-dimensional set of projected features using a projection geometry;first registering the two-dimensional set of features and thetwo-dimensional set of projected features; and second registering thetwo-dimensional image and a mathematical projection of thethree-dimensional image using parameters derived from the firstregistering.

A digital storage medium or media is capable of storing instructionsexecutable by a digital system to perform the method of the precedingparagraph.

An image registration apparatus is disclosed, comprising: a featuresdetector configured to extract a two-dimensional set of features from atwo-dimensional image and to extract a three-dimensional set of featuresfrom a three-dimensional image; a projection processor configured toproject three-dimensional data into two-dimensional projection data; anda registration processor configured to (i) adjust parameters to registerthe two-dimensional set of features and the three-dimensional set offeatures projected by the projection processor using a projectiongeometry, and to (ii) use the adjusted parameters to register thetwo-dimensional image and the three-dimensional image projected by theprojection processor using the projection geometry.

Also disclosed is an apparatus comprising: a two-dimensional imagerconfigured to acquire a two-dimensional image; a three-dimensionalimager configured to acquire a three-dimensional image; a featuresdetector configured to extract a two-dimensional set of features fromthe two-dimensional image and to extract a three-dimensional set offeatures from the three-dimensional image; a projection processorconfigured to project three-dimensional data into two-dimensionalprojection data; and a registration processor configured to register thetwo-dimensional image and the three-dimensional image projected by theprojection processor using parameters adjusted to register thetwo-dimensional set of features and the three-dimensional set offeatures projected by the projection processor.

One advantage resides in faster 2D/3D image registration.

Another advantage resides in more accurate 2D/3D image registration.

Another advantage resides in faster interventional imaging.

Still further advantages of the present invention will be appreciated tothose of ordinary skill in the art upon reading and understand thefollowing detailed description.

The drawings are only for purposes of illustrating the preferredembodiments, and are not to be construed as limiting the invention.

FIG. 1 diagrammatically shows a 2D/3D multi-modality imaging apparatus.

FIG. 2 diagrammatically shows a 3D/3D multi-modality imaging apparatuswith registration of two-dimensional projections of three-dimensionalimages acquired by the different modalities.

With reference to FIG. 1, a 2D/3D multimodality imaging system includesa two-dimensional imager 10, such as an x-ray fluoroscopy apparatus, anda three-dimensional imager 12, such as a magnetic resonance imaging(MRI) system, a computed tomography (CT) imaging system, positronemission tomography (PET) scanner, a gamma camera, and so forth. Thetwo-dimensional imager 10 is optionally capable of three-dimensionalimaging but is used in the present 2D/3D imaging system as thetwo-dimensional imager. For example, the two-dimensional imager 10 iscontemplated as a CT scanner operating without gantry rotation.

The two-dimensional imager 10 is a projection-type imager including asource 20, such as an x-ray tube in the case of an x-ray fluoroscopyapparatus, that transmits radiation 22 though an imaging region 24containing a subject (not shown), such that a generally oppositelypositioned two-dimensional detector array 26 detects the transmittedradiation as a function of position to form a two-dimensional image 30of a projection-type. The two-dimensional image 30 therefore has aprojection geometry characterized by projection parameters such asangulation, source position, detector position, or other geometricalparameters, and optionally also by projection parameters related todistortion, such as one or more distortion parameters characterizing theso-called “pincushion” distortion that is sometimes observed in x-rayfluoroscopes and other projection-type 2D imaging apparatuses. Theprojection geometry is at least approximately known, for example basedon the nominal settings of the x-ray source and detector positions. Insome embodiments, a precise calibration of the two-dimensional imager 10provides highly precise projection parameters for the projectiongeometry, including precise geometrical parameters and quantitativevalues for the distortion parameters.

The three-dimensional imager 12 acquires a three-dimensional image 32.For example, if the three-dimensional imager 12 is an MRI, it acquiressuch a three-dimensional image 32 by sampling k-spacethree-dimensionally and reconstructing the k-space samples into thethree-dimensional image 32. If the three-dimensional imager 12 is a CTscanner, it acquires projection data while the x-ray tube revolvesaround the subject, with the third dimension provided by having multiplerows of detectors (multi-slice CT) and/or by moving the patient indiscrete increments or continuously (helical CT), followed by filteredbackprojection or another reconstruction that reconstructs theprojection data into the three dimensional image 32. Other approachescan be used, depending upon the type of three-dimensional imager 12 andthe type of acquisition desired by the radiologist or other medicalprofessional.

The relationship between the projection geometry of the two-dimensionalimage 30 and the spatial frame of reference of the three-dimensionalimage 32 is known approximately, based on how the subject is positionedin the two different imagers 10, 12. In some embodiments, thisrelationship is more precisely known, for example if the two differentimagers 10, 12 are embodied integrally together as a hybrid imagingsystem, or a cross-imager mechanical alignment mechanism is used. In anycase, however, there will generally be some misregistration between thetwo-dimensional image 30 acquired by the two-dimensional imager 10, onthe one hand, and the three-dimensional image 32 acquired by thethree-dimensional imager 12, on the other hand. This misregistration cantake various forms or combinations of forms, such as rigid translationalmisregistration, rigid rotational misregistration, non-rigidtranslational and/or rotational misregistration, misregistration due topincushion distortion or other types of distortion in one or both images30, 32, and so forth. Accordingly, it is desired to mathematicallyproject the three-dimensional image 32 to form a two-dimensionalprojection image, and to register this two-dimensional projection imagewith the two-dimensional image 30 acquired by the two-dimensional imager10.

A features detector 34 processes the two-dimensional image 30 to extracta two-dimensional set of features 36 from the two-dimensional image 30.The features detector 34 also processes the three-dimensional image 32to extract a three-dimensional set of features 38 from thethree-dimensional image 32. In the embodiment of FIG. 1, the samefeatures detector 34 is applied both to the two-dimensional image 30 andto the three-dimensional image 32; however, it is also contemplated touse two different features detectors, for example, wherein one of thefeature detectors is optimized for two-dimensional images and the otherfeature detector is optimized for three-dimensional images. If twodifferent features detectors are used in this fashion, the detectedfeatures should be comparable, such as of the same type.

The features detector 34 is capable of detecting, for example, cornerfeatures suitably represented as corner points. For detecting cornerfeatures, such a feature detector 34 operates via a corner detectionalgorithm such as for example by identifying high intensity gradientregions typically corresponding to corners by identifying locallymaximum eigenvalues of an inertia matrix of the image gradient alongeach direction, and identifying a discrete set of line intersections.Advantageously, substantially the same corner features are generallydetected for both the two-dimensional and three-dimensional images, evenif the contrast mechanisms of the two imagers 10, 12 are substantiallydifferent (for example, x-ray versus magnetic resonance). Thederivative-based nature of corner detection, coupled with a highlikelihood of contrast for corner structures of the subject, ensuresthat the corner detection process is generally independent of contrasttype, contrast level, and other image characteristics. Another advantageof using corner detection by the features detector 34 is that cornerpoints are discrete in both two-dimensions and in three-dimensions.

The features detector 34 is alternatively or additionally also capableof detecting other types of features. For example, the features detector34 is alternatively or additionally capable of detecting edge features.In some embodiments, the features detector 34 is capable of detectingedge features via an edge detection algorithm which is implemented asfollows. Lines in the projected two-dimensional image correspond to theprojection of interfaces within the three-dimensional image that areoriented along the x-ray beam 22. These interfaces are suitably detectedby using the voxel intensity gradient magnitude and interface direction,along with the x-ray beam direction known from the projection geometry.The interface locations can be mapped from 3D to 2D using the projectionmatrix of Equation (2) to form a map of edge and corner locations.

In general, the features detector 34 reduces the respective images 30,32 into two-dimensional or three-dimensional sets of features 36, 38respectively, that are smaller respective subsets of data and thereforeare readily correlated in space and are more efficiently processed froma computational standpoint as compared with the full images 30, 32. Therespective sets of features 36, 38 retain the geometries of the sourceimages 30, 32. Thus, for a features detector 34 that detects cornerfeatures, the two-dimensional set of features 36 comprises a set ofpoints in a plane, while the three-dimensional set of features 38comprises a three-dimensional “cloud” of points. Similarly, for an edgedetector the two-dimensional set of features 36 comprises a set of lineslying coplanar in a plane, while the three-dimensional set of features38 comprises a three-dimensional arrangement of lines.

A projection processor 40 mathematically projects the three-dimensionalset of features 38 in accordance with a projection geometry 42 that isat least initially set to the projection geometry used by thetwo-dimensional imager 10 in acquiring the two-dimensional image 12. Foran illustrative interventional C-arm x-ray fluoroscopy apparatus such asthe Allura XPer FD10 (available from Philips Medical Systems, Eindhoven,the Netherlands), the projection geometry is suitably defined asfollows. A vector, s extends from iso-center to the x-ray source 20,while a vector d extends from iso-center to a center of the detector 26.Two normals n₁ and n₂ define the detector plane, and are known for everyprojection. Any three-dimensional point P can therefore be mapped (i.e.,projected) to a two-dimensional point p on the detector 26 given anyparticular C-arm angulation. Expanding these vectors into Cartesiancoordinates yields:

s=[s_(x), s_(y), s_(z)]^(T)

d=[d_(x), d_(y), d_(z)]^(T)

n₁=[n_(1x), n_(1y), n_(1z)]^(T)

n₂=[n_(2x), n_(2y), n_(2z)]^(T)

P=[X, Y, Z]^(T)

p=[u, v, μ]^(T)  (1).

The matrix vector equation defining the projection geometry 42 can bewritten as:

$\begin{matrix}{\begin{bmatrix}u \\v \\\mu\end{bmatrix} = {{\begin{bmatrix}n_{1x} & n_{2x} & {s_{x} - X} \\n_{1y} & n_{2y} & {s_{y} - Y} \\n_{1z} & n_{2z} & {s_{z} - Z}\end{bmatrix}^{- 1}\begin{bmatrix}{s_{x} - d_{x}} \\{s_{y} - d_{y}} \\{s_{z} - d_{z}}\end{bmatrix}}.}} & (2)\end{matrix}$

Equation (2) is applied by the projection processor 40 to eachthree-dimensional corner point P (in the case of a corner detector) ofthe three-dimensional set of features 38 using the selected projectiongeometry 42 to generate corresponding points p of a two-dimensional setof projected features 44.

A registration processor 46 registers the two-dimensional set ofprojected features 44 with the two-dimensional set of features 36extracted from the two-dimensional image 30. If the registration entailsadjusting projection parameters, then this registration process isoptionally iterative, following an iteration loop 48 to re-project thethree-dimensional set of features 38 using projection parametersadjusted by the registration in an iterative manner. The output of theregistration processor 46 is a set of one or more registrationparameters 50. The registration may entail adjustment of variousparameters such as projection parameters (e.g., angulation,magnification, source/detector locational parameters, a parameterquantifying pincushion distortion, or so forth), rigid translations orrotations, nonrigid translations or rotations, and so forth. Theregistration may entail selecting or refining projection parameters ofthe projection geometry used for the mathematical projecting operation.However, computing the registration parameters 50 based on the completeimages 30, 32 is computationally intensive, especially for iterativeregistration techniques.

The registration parameters 50 are efficiently adjusted by theadjustment processor 46 (optionally including iterative re-projectionvia loop 48 and projection processor 40) respective to the smaller setsof features 36, 38. As one example, the two-dimensional set of features36 extracted from the two-dimensional image 30 are taken as thereference, and projection parameters of the projection geometry 42and/or spatial parameters of the two-dimensional set of projectedfeatures 44 are adjusted.

If the projection geometry 42 is accurately or precisely known, forexample based on calibrations of the two-dimensional imager 10, and onlyrigid registration is to be performed, then the optimization spaceincludes only six parameters, e.g. three rotations and threetranslations respective to the spatial parameters of the two-dimensionalset of projected features, and the registration processor 46 can employa downhill simplex method for numerical adjustment and optimization ofthese six parameters. The adjustment or optimization is suitablyrespective to a similarity measure computed (for example) as a sum ofthe distance squared between each corner point in the two-dimensionalset of features 36 and the corresponding projected corner point in thetwo-dimensional set of projected features 44.

If the projection geometry 42 is not known with sufficient accuracy orprecision, then the registration processor 46 optionally adjustsprojection parameters of the projection geometry 42 as part of theregistration. For example, the projection processor 40 is applied to thethree-dimensional set of features 38 with a plurality of differentprojection angulations deviating by selected amounts from the nominalangulation used in acquiring the two-dimensional image 30. Theregistration is applied to the two-dimensional set of projected features44 generated by the mathematical projection at each selected angulation,the “best fit” registration is selected, and the angulationcorresponding to the best fit is selected as the adjusted angulation ofthe adjusted projection geometry 42. This brute force approach isfeasible because the dimensionality reduction provided by registeringonly the features (e.g., corner points) rather than registering entireimages provides fast processing. Additionally or alternatively, theangulation or other projection parameters can be included as parametersthat are optimized by the registration processor 46 using a leastsquares minimization or another optimization technique. Optionaliterative or exhaustive registration in which the registration processor46 is applied to different two-dimensional sets of projected features 44generated by the projection processor 40 with different mathematicalprojection angulations (or with other variations in the projectiongeometry 42) are diagrammatically indicated in FIG. 1 by an iterationloop arrow 48.

In most situations, it is anticipated that the projection geometry ofthe two-dimensional image 30 will be known with a relatively high degreeof accuracy, for example based on a calibrated projection geometry ofthe two-dimensional imager 10 used in acquiring the two-dimensionalimage 30. In such embodiments, it is generally suitable to assume thateach feature in the two-dimensional set of features 36 and the closestfeature in the two-dimensional set of projected features 44 bothcorrespond to the same corner point of the subject. In such a case, thesimilarity measure optimized by the registration processor 46 issuitably computed as a sum of distances-squared where each distance isbetween a feature of the set of two-dimensional features 36 and theclosest feature of the set of two-dimensional projected features 44.

It is contemplated, however, that in some situations, the projectiongeometry of the two-dimensional image 30 will be known with sufficientlylimited precision and/or accuracy that it is not reasonable to assumethat each feature in the two-dimensional set of features 36 and theclosest feature in the two-dimensional set of projected features 44 bothcorrespond to the same corner point of the subject. In such cases, it iscontemplated for the registration processor 46 to apply a combinatoricalgorithm to associate features of the two-dimensional set of projectedfeatures 44 with corresponding features of the two-dimensional set offeatures 36 extracted from the two-dimensional image 30.

As further shown in FIG. 1, the adjusted registration parameters 50 areused by an image projector and adjuster 52 to register the respective 2Dand 3D images 30, 32 in a second registration step. The registrationparameters 50 are adjusted respective to the two-dimensional set offeatures 36 and the two-dimensional set of projected features 44;however, these features 36, 44 are representative of the spatialcharacteristics of the respective two- and three-dimensional images 30,32 and accordingly the registration parameters 50 are applicable in thesecond registration process performed by the image projector andadjuster 52, which projects the three-dimensional image 32 and adjuststhe projected image in accordance with the projection geometry 42 andthe registration parameters 50 to produce a two-dimensional projectedand registered image 54, which is registered with the two-dimensionalimage 30.

The projection performed by the image projector and adjuster 52 canemploy substantially any type of 3D-to-2D projection method, such as adigitally reconstructed radiograph (DRR) method that sets each point inthe projection plane to the line integral mathematically calculatedalong the line connecting the (virtual) source with the (virtual) pointin the projection plane. Other projection methods are also contemplated,such as a maximum intensity projection (MIP) that sets each point in theprojection plane to the largest value along the line connecting the(virtual) source with the (virtual) point in the projection plane.

The two-dimensional projected image 54 is suitably compared or combinedwith the two-dimensional image 30 acquired by the two-dimensional imager10 by an image processor 56, such as an image combiner or fusionprocessor, an image comparator, an image display (such as a userinterface with a graphical display) and so forth. For example, the 2Dimage 30 and the 2D projected and registered image 54 can be fused by animage fusion technique and the fused image displayed, or the two images30, 54 can be displayed side-by-side or in a vertical arrangement. Inthe latter case, it is contemplated to have locked pointers of a mouseor other pointing device that are displayed at the same spatial positionin both of the two displayed images 30, 54 so that a radiologist canreadily locate corresponding features in the two images 30, 54.

Although the described registration process is expected to provideaccurate and precise results in many cases, in some instances theresulting image registration may be less than fully satisfactory. Insome situations, the registered two-dimensional images 30, 54 arecompared and, if not aligned within a preselected threshold or to thesatisfaction of the radiologist, then subjected to another imageregistration procedure such as an intensity-based image registrationprocedure performed by the image processor 56 or another component.

One contemplated application for the multimodality imaging system ofFIG. 1 is in the area of interventional cardiac electrophysiology.Interventional cardiac electrophysiology procedures are typicallyperformed under x-ray fluoroscopy for visualizing catheters or otherinterventional devices relative to highly attenuating structures of thepatient such as the thoracic spine and ribs. These projections do nothowever contain information about soft-tissue anatomy. Accordingly, itis advantageous to fuse these two-dimensional x-ray fluoroscopy imageswith pre-operatively acquired volumetric cardiac helical or multi-sliceCT or MRI images. The system of FIG. 1 provides rapid fusion of thetwo-dimensional x-ray fluoroscopy images (corresponding to thetwo-dimensional images 30) with three-dimensional CT or MRI images(corresponding to the three-dimensional images 32) by the imagecomparator or combiner 56 (which in this embodiment is an image fusionprocessor) so as to provide real-time visualization of the catheterspresent within the thorax in relation to the cardiovascular anatomyvisible in the volumetric dataset 32.

With reference to FIG. 2, in another contemplated application bothimagers of the multimodality imaging system are capable of, and used to,generate three-dimensional images. In other words, in the embodiment ofFIG. 2 the two-dimensional imager 10 is replaced by a secondthree-dimensional imager 60, which may be of the same modality or adifferent modality as compared with the three-dimensional imager 12. Theprojection processor 40 is applied to the three-dimensional imagegenerated by the second three-dimensional imager 60 to produce thetwo-dimensional image 30 having the projection geometry 42, which inthis embodiment is a selected geometry used for the mathematicalprojection of the three-dimensional image generated by the secondthree-dimensional imager 60. From that point on, the components andprocessing of the multimodality imaging system of FIG. 2 is analogous tothat of the multimodality imager of FIG. 1. The approach of FIG. 2 canprovide rapid registration of digitally reconstructed radiographs (DRRs)or other projections generated by different three-dimensional imagers,for example by CT and MRI imagers or by two different CT imagers. Fasterprocessing is possible because the registration is performed in 2D, andmoreover is performed only respective to the small features datasets 36,44.

Those skilled in the art will readily appreciate that the imageregistration processes disclosed herein can be embodied by a digitalstorage medium or media storing instructions executable by a digitalsystem to perform the disclosed method. For example, the digital storagemedium or media can a magnetic disk, optical disk, magnetic tape, FLASHmemory or other electrostatic memory, random access memory (RAM),read-only memory (ROM), Internet server, or so forth, or a combinationof such media, and the stored instructions can be executable on adigital system such as a computer, digital network, Internet server, orso forth.

The preferred embodiments have been described. Modifications andalterations may occur to others upon reading and understanding thepreceding detailed description. It is intended that the invention beconstrued as including all such modifications and alterations insofar asthey come within the scope of the appended claims or the equivalentsthereof.

1. An image registration process comprising: extracting atwo-dimensional set of features (36) from a two-dimensional image (30);extracting a three-dimensional set of features (38) from athree-dimensional image (32); mathematically projecting thethree-dimensional set of features into a two-dimensional set ofprojected features (44) using a projection geometry; first registeringthe two-dimensional set of features and the two-dimensional set ofprojected features; and second registering the two-dimensional image anda mathematical projection of the three-dimensional image usingparameters derived from the first registering.
 2. The image registrationprocess as set forth in claim 1, wherein the extracting operationscomprise: applying a corner detection algorithm to extract featurescomprising corner points.
 3. The image registration process as set forthin claim 1, wherein the extracting operations comprise: applying an edgedetection algorithm to extract features comprising line segments.
 4. Theimage registration process as set forth in claim 1, wherein the firstregistering comprises: adjusting spatial parameters of at least one ofthe two-dimensional set of features (36) and the two-dimensional set ofprojected features (44) selected from a group consisting of at leastthree rotation parameters and at least three translation parameters, theadjusted spatial parameters being used in the second registering.
 5. Theimage registration process as set forth in claim 1, wherein the firstregistering comprises: adjusting one or more parameters of theprojection geometry (42) selected from a group consisting of anangulation parameter, a magnification parameter, a source locationparameter, a detector location parameter, and a distortion parameter,the adjusted parameters of the projection geometry being used in thesecond registering for the mathematical projection of thethree-dimensional image.
 6. The image registration process as set forthin claim 1, wherein the first registering comprises: optimizing adistance figure-of-merit statistically characterizing distances betweenfeatures of the two-dimensional set of features (36) and closestcorresponding features of the two-dimensional set of projected features(44).
 7. The image registration process as set forth in claim 1, whereinthe projection geometry (42) is a projection geometry of atwo-dimensional imager (10) during acquisition of the two-dimensionalimage (30).
 8. The image registration process as set forth in claim 1,wherein the first registering applies a combinatoric algorithm toassociate features of the two-dimensional set of features (36) withcorresponding features of the two-dimensional set of projected features(44).
 9. The image registration process as set forth in claim 1, furthercomprising: acquiring the two-dimensional image (30) using atwo-dimensional imager (10) comprising an x-ray fluoroscopy apparatus;and acquiring the three-dimensional image (32) using a three-dimensionalimager (12) comprising a magnetic resonance imager (MRI) or a computedtomography (CT) imager.
 10. The image registration process as set forthin claim 1, further comprising: acquiring a second three-dimensionalimage; and forming the two-dimensional image (30) by mathematicallyprojecting the second three-dimensional image using the projectiongeometry (42).
 11. The image registration process as set forth in claim1, further comprising: displaying a combination, fusion, or comparisonof the two-dimensional image and a mathematical projection of thethree-dimensional image after the second registering using theparameters derived from the first registering.
 12. A digital storagemedium or media storing instructions executable by a digital system toperform the method of claim
 1. 13. An image registration apparatusincluding one or more processors programmed to perform the method ofclaim
 1. 14. An image registration apparatus comprising: a featuresdetector (34) configured to extract a two-dimensional set of features(36) from a two-dimensional image (30) and to extract athree-dimensional set of features (38) from a three-dimensional image(32); a projection processor (40) configured to projectthree-dimensional data into two-dimensional projection data; and aregistration processor (46, 52) configured to (i) adjust parameters toregister the two-dimensional set of features and the three-dimensionalset of features projected by the projection processor using a projectiongeometry (42), and to (ii) use the adjusted parameters to register thetwo-dimensional image and the three-dimensional image projected by theprojection processor using the projection geometry.
 15. The imageregistration apparatus as set forth in claim 14, wherein the featuresdetector (34) comprises a corner detector.
 16. The image registrationapparatus as set forth in claim 14, wherein the features detector (34)comprises an edge detector.
 17. The image registration apparatus as setforth in claim 14, wherein the registration processor (46, 52) isconfigured to adjust the projection geometry (42) to register orcontribute to registering the two-dimensional set of features (36) andthe three-dimensional set of features (38) projected by projectionprocessor (40).
 18. The image registration apparatus as set forth inclaim 14, wherein the registration processor (46, 52) is configured toadjust spatial parameters of at least one of the two-dimensional set offeatures (36) and the three-dimensional set of features (38) projectedby projection processor (40) to register or contribute to registeringthe two-dimensional set of features and the three-dimensional set offeatures projected by the projection processor.
 19. The imageregistration apparatus as set forth in claim 14, wherein the projectiongeometry (42) is a predetermined projection geometry of a projectionimager (10) used to acquire the two-dimensional image (30).
 20. Anapparatus comprising: a two-dimensional imager (10) configured toacquire a two-dimensional image (30); a three-dimensional imager (12)configured to acquire a three-dimensional image (32); a featuresdetector (34) configured to extract a two-dimensional set of features(36) from the two-dimensional image and to extract a three-dimensionalset of features (38) from the three-dimensional image; a projectionprocessor (40) configured to project three-dimensional data intotwo-dimensional projection data; and a registration processor (46, 52)configured to register the two-dimensional image and thethree-dimensional image projected by the projection processor usingparameters adjusted to register the two-dimensional set of features andthe three-dimensional set of features projected by the projectionprocessor.
 21. The apparatus as set forth in claim 20, wherein theregistration processor (46, 52) adjusts one or more parameters selectedfrom a group consisting of (i) projection geometry parameters, (ii)two-dimensional translation parameters, and (iii) two-dimensionalrotation parameters to register the two-dimensional set of features (36)and the three-dimensional set of features (38) projected by theprojection processor (40).
 22. The apparatus as set forth in claim 20,wherein the two-dimensional imager (10) comprises an x-ray fluoroscopyapparatus.